Title: An experimental study of Stockwell transform-based feature extraction method for ischemic stroke detection

Authors: P.V. Jayaram; R. Menaka

Addresses: School of Electronics Engineering, VIT University, Vellore, India ' School of Electronics Engineering, VIT University, Vellore, India

Abstract: This work proposes an approach to detect the presence of ischemic lesion in the tissue part of the brain. Accurate classification and segmentation of stroke affected regions are essential for quick diagnosis. Image classification is an important step for high-level processing of automatic brain stroke classification. The proposed method employs Skull Elimination Algorithm (SEA), Central Line Sketching Algorithm (CLSA), Fuzzy C-means (FCM) clustering-based segmentation and Discrete Orthonormal Stockwell Transform (DOST). The skull elimination and CLSAs are the main stages of preprocessing. The skull elimination was mainly adopted for extracting only the tissue part in the brain and the CLSA is used for splitting the Magnetic Resonance Image (MRI) into two equal sections. FCM-based segmentation is mainly used for extracting the lesion part. Then in the next stage DOST is applied into left and right sections of brain image for extracting the features such as mean, median and standard deviation which classifies the normal and abnormal MRI.

Keywords: ischemic lesion; brain tissue; SEA; skull elimination algorithm; CLSA; central line sketching algorithm; DOST; discrete orthonormal Stockwell transform; FCM; fuzzy C-means clustering; feature extraction; ischemic stroke detection; image classification; medical images; magnetic resonance imaging; MRI scans; brain images.

DOI: 10.1504/IJBET.2016.076731

International Journal of Biomedical Engineering and Technology, 2016 Vol.21 No.1, pp.40 - 48

Received: 29 Jun 2015
Accepted: 04 Oct 2015
Published online: 24 May 2016 *

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